Scheduling Optimization Cell Formation Problem for Cellular Manufacturing System Using Meta-Heuristic Methods

Abstract:

In Cellular manufacturing industries are producing similar products using cells, or groups of team members, workstations, or equipment, to make easy operations by eliminating setup and unnecessary costs between operations, Cells might be designed for a specific process, part, or a complete product. There is a strong propensity towards the effectiveness of manufacturing system, proper scheduling (determining the sequence of operations is to be performed) of jobs is essential for the flourishing operation of a shop. Group technology has become a more and more popular concept in manufacturing, which is designed to take advantage of mass production layout and techniques in smaller batch production system. Since the conventional scheduling methods need more computation time. In this paper, an effort has been made in two parts from the first part of this work is to optimize scheduling in different types of products in the job-shop environment are identified and grouping of cells is performed using Rank Order Clustering Method. In the second part, optimization procedure has been developed for the scheduling problem for processing in the machine cells. Particle Swarm Optimization and Genetic Algorithm are used in this paper for explore the optimum schedule by minimizing the total penalty cost due to the delay in meeting the due date. Better scheduling is obtained by comparing the two methods.

• Periodical: Applied Mechanics and Materials (Volume 786)
• Pages: 340-344
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.786.340
• Online since: August 2015
• Authors: M. Saravanan, S. Karthikeyan*
• Keywords: Cellular Manufacturing System, Genetic Algorithm (GA), Job Shop Environment, Particle Swarm Optimization, Rank Order Clustering Techniques, Scheduling

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